Maximum-entropy closures for kinetic theories of neuronal network dynamics

We analyze (1+1)D kinetic equations for neuronal network dynamics, which are derived via an intuitive closure from a Boltzmann-like equation governing the evolution of a one-particle (i.e., one-neuron) probability density function. We demonstrate that this intuitive closure is a generalization of moment closures based on the maximum-entropy principle. By invoking maximum-entropy closures, we show how to systematically extend this kinetic theory to obtain higher-order, (1+1)D kinetic equations and to include coupled networks of both excitatory and inhibitory neurons.